Multiple sclerosis (MS) is an inflammatory disease of the brain and spinal cord characterized by demyelinating lesions that are most easily identified, at least on magnetic resonance imaging (MRI) studies, in the white matter of the brain. Quantitative analyses of MRI, such as the number and volume of lesions, are essential for evaluating disease-modifying therapies and monitoring disease progression. MRI measures are also a common primary endpoint in phase II immunomodulatory drug therapy trials. In practice, lesion burden is determined by visual examination and delineation of serial MRI. Manual delineation is challenging and time consuming, as three-dimensional information from several MRI modalities must be integrated. As manual assessment of MRI is also prone to large inter- and intra-observer variability, a sensitive and specific automated method to detect lesions in the brain is essential for the analysis of large MS studies.
One such study provides a comprehensive review of currently available automated cross-sectional MS lesion segmentation methods, or methods used to identify lesions from a single MRI study. These methods are divided into four categories: supervised classifier with an atlas, supervised classifier with no atlas, unsupervised classifier with an atlas, and unsupervised classifier with no atlas.
Supervised methods with atlases involve registering volumes to a statistical or topological atlas containing prior information about lesion location. Supervised classification methods are then applied to the volumes: artificial neural networks, k-nearest neighbors, fast trimmed likelihood estimator and hidden Markov chains, principal components analysis, Fisher linear discriminant and decision forests, and Bayes.
Supervised methods without atlases train on manually-segmented images annotated by experts and use image intensities of MRI to classify lesions. Supervised classification algorithms are applied to the volumes: artificial neural networks, spatial clustering, k-nearest neighbors, Parzen window, Parzen window and morphological gray scale reconstruction, Bayes, AdaBoost, simulated annealing and Markov random fields, and graph cuts.
Unsupervised methods with atlases involve registering volumes to a statistical or topological atlas and then applying unsupervised classification methods to the volumes: expectation maximization and Markov random fields, region partitioning, expectation maximization and Gaussian mixture models, and fuzzy C-means.
Unsupervised methods without atlases apply unsupervised classification methods to the volumes: fuzzy C-means, expectation maximization and constrained Gaussian mixture models, Bayes, adaptive mixtures method and Markov random fields, expectation maximization and mean shift, and expectation maximization.
Almost all of the aforementioned methods, use multi-modality MRI information to classify lesions. The most widely-used feature across all segmentation methods is voxel intensity, which derives strength from a multi-modality approach.
One difficulty in automated segmentation of MRI is due to variable imaging acquisition parameters. All of the aforementioned methods have tuning parameters that are adjusted to a particular data set and may not generalize to a new data set with different acquisition parameters. These parameters are not informed by the data and therefore must be tuned empirically, often with little to no interpretability of the parameter. Application to a new data set may require several iterations of segmentations to adjust the tuning parameters to values that produce acceptable segmentations. A method in which the tuning parameters are informed by the data and for which adjustments are intuitive and simple would therefore be valuable.
A second difficulty in intensity-based segmentation is that MRI data are acquired in arbitrary units; units can vary widely between and within imaging centers. These variations are attributed to scanner hardware, interactions between hardware and patients, and variations in acquisition parameters. Therefore, proper intensity normalization is essential in developing a robust segmentation method. Many of the segmentation methods use intensity-normalized images, but these methods do not demonstrate robustness of the normalization procedure to changes in imaging acquisition parameters and imaging centers.
A third difficulty is intensity inhomogeneity, the slow spatial intensity variations of the same tissue within an MRI volume. Inhomogeneity can significantly reduce the accuracy of image segmentation, and therefore some form of spatial normalization is necessary for accurate lesion segmentation. Most lesion segmentation methods assume that these inhomogeneities have been corrected during image preprocessing, while it has been found that strong spatial patterns within tissue type even after the N3 inhomogeneity correction algorithm is applied few researchers account for image inhomogeneities in their model.
It would therefore be advantageous to provide an easy to use, fully automated, robust, and novel statistical method for cross-sectional MS lesion segmentation. Using intensity information from multiple modalities of MRI, a logistic regression model assigns voxel-level probabilities of lesion presence.